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Introduction to Cloud Computing

This module will be partially interactive. Please follow along on your own computer.

What is a cloud?

A "cloud" is a computer system that provides users with shared access to on-demand computing resources via the internet.

Goals/Benefits

  • Resource utilization
  • Scalability and Elasticity
  • Reproducibility by way of Programmability
  • Reliability through redundancy

Cloud Means Different Things to Different Groups

Different kinds of people use clouds for different purposes.

  • System Administrators - use cloud to automate operations.
  • Software Developers - build applications on cloud servers and platforms.
  • Computational scientists - Write codes to analysis scientific data in the cloud.

In this course we will try to exposure you to each of these views.

Cloud service models

Clouds can offer different service models:

  • Infrastructure-as-a-service (Iaas) - virtual servers, networks, firewalls, etc. (Openstack, Jetstream, AWS, Azure)
  • Platform-as-a-service (Paas) - deploy applications without managing virtual servers (Agave, Google App Engine, Heroku)
  • Software-as-a-service (Saas) - Ready to use software application (Jupyterhub, Gmail, Office365)

Also: emerging models such as functioni-as-a-service.

Infrastrcture-as-a-service

Key concepts:

  • Virtual Machines (VMs) - Simulate a physical computer through software.
  • Software defined networking: Routers, networks and subnets - used to connect VMs to other computers.
  • Security groups - firewall rules enabling or disabling network traffic to/from ports on the VMs.

Intro to Docker

Intro slides: https://docs.google.com/presentation/d/1RVyQ1CDamNLRw9mIcUec-IoTcH0-wbQcXsjbRB5NAws/edit?usp=sharing

Initial setup

Typically, accessing the docker daemon requires root or to be in the docker group. For the purposes of this introduction, we can simply do everything as the root user:

$ sudo su - root

Make sure you can access the docker daemon; you can verify this by checking the version:

$ docker version
Client:
 Version:	17.12.1-ce
 API version:	1.35
 Go version:	go1.10.1
 Git commit:	7390fc6
 Built:	Wed Apr 18 01:23:11 2018
 OS/Arch:	linux/amd64

Server:
 Engine:
  Version:	17.12.1-ce
  API version:	1.35 (minimum version 1.12)
  Go version:	go1.10.1
  Git commit:	7390fc6
  Built:	Wed Feb 28 17:46:05 2018
  OS/Arch:	linux/amd64
  Experimental:	false

Create a test directory to contain your docker work:

$ mkdir docker; cd docker

Docker Images and Tags, Docker Hub, and Pulling Images

A Docker image is a container template from which one or more containers can be run. It is a rooted filesystem that, by definition, contains all of the file dependencies needed for whatever application(s) will be run within the containers launched from it. The image also contains metadata describing options available to the operator running containers from the image.

One of the great things about Docker is that a lot of software has already been packaged into Docker images. One source of 100s of thousands of public images is the official docker hub: https://hub.docker.com.

The docker hub contains images contributed by individual users and organizations as well as "official images". Explore the official docker images here: https://hub.docker.com/explore/

Most modern programming languages offer an official image. For example, there is an official image for the Python programming language: https://hub.docker.com/_/python/

Docker supports the notion of image tags, similar to tags in a git repository. Tags identify a specific version of an image.

The full name of an image on the Docker Hub is comprised of components separated by slashes. The components include a "repository" (which could be owned by an individual or organization), the "name", and the "tag". For example, an image with the full name

jstubbs/test:0.1

would be the "test" in the "jstubbs" repository and have a tag of "0.1". TACC maintains multiple repositories on the Docker Hub including:

tacc
taccsciapps
agaveapi
abaco

Official images such as the python official image are not owned by a repository, but all other images are.

To pull an image off Docker Hub use the docker pull command

$ docker pull python
Using default tag: latest
latest: Pulling from library/python
cc1a78bfd46b: Pull complete
. . .

As indicated in the output, if no tag is specified the "latest" tag is pulled. You can verify that the image is available on your local machine using the docker images command:

$ docker images
REPOSITORY             TAG                 IMAGE ID            CREATED             SIZE
jstubbs/test           latest              9dfe5a2c4b43        52 minutes ago      81.2 MB
python                 latest              a5b7afcfdcc8        3 hours ago         912 MB

Running a Docker Container

We use the docker run command to run containers from an image. We pass a command to run in the container. Similar to running other progams on Unix systems, we can run containers in the foreground (attached) or in the backgrouns.

Running and Attaching to a Container

To run a container and attach to it in one command, use the -it flags. Here we run bash in a container from the ubuntu image:

docker run -it ubuntu bash

Running a Container in Daemon mode

We can also run a container in the background. We do so using the -d flag:

docker run -d ubuntu sleep infinity

Keep in mind that the command given to the docker run statement will be given PID 1 in the container, and as soon as this process exits the container will stop.

Installing and Running the Containerized Classifier Application

We have packaged up a self-contained image classifier application based on the TensorFlow library. Our image is available from the DockerHub. To download it, issue the following commnand:

$ docker pull taccsciapps/classify_image

What you are downloading is a complete application that will run an image classifier algorithm on an image that is publicly available from the internet.

Once the download is complete, we can run one or more containers from our image. This particular image requires an environment variable, $URL, containing the address to a publicly available image, to be passed in at run time.

For example, we can run our image classifier program on the 3 different images:

$ docker run -it --rm -e URL=https://raw.githubusercontent.com/TACC/taccster18_Cloud_Tutorial/master/classifier/data/dog.jpeg taccsciapps/classify_image
$ docker run -it --rm -e URL=https://www.dropbox.com/s/f93aixy0r5f1fz1/jimo.jpeg?raw=1 taccsciapps/classify_image
$ docker run -it --rm -e URL=https://www.dropbox.com/s/80o4vbzrz4eyzp0/mpackard.jpg?raw=1 taccsciapps/classify_image

Additional Remarks on Running Containers

Running Additional Commands in a Running Container

Finally, we can execute commands in a running container using the docker exec command. First, we need to know the container id, which we can get through the docker ps command:

~ $ docker ps
CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES
a2f968b8443f        ubuntu:16.04        "sleep infinity"    9 seconds ago       Up 8 seconds                            awesome_goldwasser

Here we see the container id is a2f968b8443f. To execute bash in this container we do:

docker exec -it a2f968b8443f bash

At this point we are attached to the running container. If our bash session exits, the container will keep running because the sleep infinity command is still running.

*Note: The docker ps command only shows you running containers - it does not show you containers that have exited. In order to see all containers on the system use docker ps -a.

Removing Docker Containers

We can remove a docker container using the docker rm command (optionally passing -f to "force" the removal if the container is running). You will need the container name or id to remove the container:

$ docker rm -f a2f968b8443f

Building Images From a DockerFile

We can build images from a text file called a Dockerfile. You can think of a Dockerfile as a recipe for creating images. The instructions within a dockerfile either add files/folders to the image, add metadata to the image, or both.

The FROM instruction

We can use the FROM instruction to start our new image from a known image. This should be the first line of our Dockerfile. We will start our image from an official Ubuntu 16.04 image:

FROM ubuntu:16.04

The RUN instruction

We can add files to our image by running commands with the RUN instruction. We will use that to install wget via apt. Keep in mind that the the docker build cannot handle interactive prompts, so we use the -y flag in apt. We also need to be sure to update our apt packages.

The Dockerfile will look like this now:

FROM ubuntu:16.04

RUN apt-get update && apt-get install -y wget

The ADD instruction

We can also add local files to our image using the ADD instruction. We can add a file test.txt in our local directory to the /root directory in our container with the following instruction:

ADD test.txt /root/text.txt

A complete Dockerfile for the classify_image application as well as the necessary scripts and supporting files is available from the Tutorial gihub repo: https://github.com/TACC/taccster18_Cloud_Tutorial/tree/master/classifier

Building the Image

To build an image from a Dockerfile we use the docker build command. We use the -t flag to tag the image: that is, give our image a name. We also need to specify the working directory for the buid. We specify the current working directory using a dot (.) character:

docker build -t classify_image .

Note that this command requires all the files in the classifier directory in the Tutorial github repo (https://github.com/TACC/taccster18_Cloud_Tutorial/tree/master/classifier)

Jupyter

Online notebooks and access to other TACC resources.

(https://jupyter.tacc.cloud)

Atmosphere (Jetstream Project)

1-Click access to VMs.

(https://use.jetstream-cloud.org)